Load Data
dataset <- read.delim("raw_data/Figure5A.txt", stringsAsFactors = FALSE)
dataset$genotype <- gsub("Δ","d",gsub(" ", "", dataset$genotype))
dataset$genotype <- factor(dataset$genotype)
dataset$Experiment <- factor(rep(paste0("exp", 1:(length(dataset$genotype)/length(levels(dataset$genotype)))), each=length(unique(dataset$genotype))))
dataset$BRCA <- factor(gsub("\\+.*","",dataset$genotype))
dataset$siRNA <- factor(gsub(".*\\+","",dataset$genotype))
dataset$UID <- factor(paste(dataset$Experiment, dataset$siRNA, dataset$BRCA))
dataset$GSID <- factor(paste(dataset$siRNA, dataset$BRCA))
# wide format
kable(dataset, row.names = F)
| d11/heterozyg+siCtrl |
1816 |
1532 |
1328 |
1276 |
exp1 |
d11/heterozyg |
siCtrl |
exp1 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
| d11/heterozyg+siALC1 |
1288 |
820 |
772 |
560 |
exp1 |
d11/heterozyg |
siALC1 |
exp1 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
| d11/d11+siCtrl |
1040 |
632 |
356 |
51 |
exp1 |
d11/d11 |
siCtrl |
exp1 siCtrl d11/d11 |
siCtrl d11/d11 |
| d11/d11+siALC1 |
600 |
296 |
220 |
17 |
exp1 |
d11/d11 |
siALC1 |
exp1 siALC1 d11/d11 |
siALC1 d11/d11 |
| d11/heterozyg+siCtrl |
1240 |
1196 |
1036 |
964 |
exp2 |
d11/heterozyg |
siCtrl |
exp2 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
| d11/heterozyg+siALC1 |
1900 |
1048 |
840 |
656 |
exp2 |
d11/heterozyg |
siALC1 |
exp2 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
| d11/d11+siCtrl |
956 |
360 |
304 |
5 |
exp2 |
d11/d11 |
siCtrl |
exp2 siCtrl d11/d11 |
siCtrl d11/d11 |
| d11/d11+siALC1 |
832 |
336 |
106 |
4 |
exp2 |
d11/d11 |
siALC1 |
exp2 siALC1 d11/d11 |
siALC1 d11/d11 |
| d11/heterozyg+siCtrl |
1162 |
1088 |
940 |
889 |
exp3 |
d11/heterozyg |
siCtrl |
exp3 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
| d11/heterozyg+siALC1 |
1232 |
832 |
605 |
356 |
exp3 |
d11/heterozyg |
siALC1 |
exp3 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
| d11/d11+siCtrl |
984 |
580 |
324 |
10 |
exp3 |
d11/d11 |
siCtrl |
exp3 siCtrl d11/d11 |
siCtrl d11/d11 |
| d11/d11+siALC1 |
724 |
320 |
124 |
6 |
exp3 |
d11/d11 |
siALC1 |
exp3 siALC1 d11/d11 |
siALC1 d11/d11 |
library(reshape2)
# reshape to long format
dataset <- melt(dataset, variable.name = "Treatment", value.name = "Counts")
dataset$siRNA <- relevel(dataset$siRNA, ref = "siCtrl")
dataset$BRCA <- relevel(dataset$BRCA, ref = "d11/heterozyg")
dataset$UID <- relevel(dataset$UID, ref = "exp1 siCtrl d11/heterozyg")
dataset$Olaparib <- gsub("NT","1",dataset$Treatment)
dataset$Olaparib <- gsub("X|nM","",dataset$Olaparib)
dataset$Olaparib <- log10(as.integer(dataset$Olaparib))
dataset$Offset <- NA
for(uid in levels(dataset$UID)){
dataset$Offset[dataset$UID == uid] <- mean(dataset$Counts[dataset$UID == uid])
}
dataset$NormCounts <- dataset$Counts / dataset$Offset
dataset$Offset2 <- NA
for(gsid in levels(dataset$GSID)){
dataset$Offset2[dataset$GSID == gsid] <- mean(dataset$NormCounts[dataset$GSID == gsid & dataset$Olaparib == 0])
}
dataset$NormCounts2 <- dataset$NormCounts / dataset$Offset2
# long format
kable(dataset, row.names = F)
| d11/heterozyg+siCtrl |
exp1 |
d11/heterozyg |
siCtrl |
exp1 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
NT |
1816 |
0.000000 |
1488.00 |
1.2204301 |
1.159350 |
1.0526849 |
| d11/heterozyg+siALC1 |
exp1 |
d11/heterozyg |
siALC1 |
exp1 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
NT |
1288 |
0.000000 |
860.00 |
1.4976744 |
1.612312 |
0.9288986 |
| d11/d11+siCtrl |
exp1 |
d11/d11 |
siCtrl |
exp1 siCtrl d11/d11 |
siCtrl d11/d11 |
NT |
1040 |
0.000000 |
519.75 |
2.0009620 |
2.142651 |
0.9338719 |
| d11/d11+siALC1 |
exp1 |
d11/d11 |
siALC1 |
exp1 siALC1 d11/d11 |
siALC1 d11/d11 |
NT |
600 |
0.000000 |
283.25 |
2.1182701 |
2.396373 |
0.8839484 |
| d11/heterozyg+siCtrl |
exp2 |
d11/heterozyg |
siCtrl |
exp2 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
NT |
1240 |
0.000000 |
1109.00 |
1.1181244 |
1.159350 |
0.9644409 |
| d11/heterozyg+siALC1 |
exp2 |
d11/heterozyg |
siALC1 |
exp2 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
NT |
1900 |
0.000000 |
1111.00 |
1.7101710 |
1.612312 |
1.0606948 |
| d11/d11+siCtrl |
exp2 |
d11/d11 |
siCtrl |
exp2 siCtrl d11/d11 |
siCtrl d11/d11 |
NT |
956 |
0.000000 |
406.25 |
2.3532308 |
2.142651 |
1.0982797 |
| d11/d11+siALC1 |
exp2 |
d11/d11 |
siALC1 |
exp2 siALC1 d11/d11 |
siALC1 d11/d11 |
NT |
832 |
0.000000 |
319.50 |
2.6040689 |
2.396373 |
1.0866709 |
| d11/heterozyg+siCtrl |
exp3 |
d11/heterozyg |
siCtrl |
exp3 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
NT |
1162 |
0.000000 |
1019.75 |
1.1394950 |
1.159350 |
0.9828741 |
| d11/heterozyg+siALC1 |
exp3 |
d11/heterozyg |
siALC1 |
exp3 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
NT |
1232 |
0.000000 |
756.25 |
1.6290909 |
1.612312 |
1.0104067 |
| d11/d11+siCtrl |
exp3 |
d11/d11 |
siCtrl |
exp3 siCtrl d11/d11 |
siCtrl d11/d11 |
NT |
984 |
0.000000 |
474.50 |
2.0737619 |
2.142651 |
0.9678484 |
| d11/d11+siALC1 |
exp3 |
d11/d11 |
siALC1 |
exp3 siALC1 d11/d11 |
siALC1 d11/d11 |
NT |
724 |
0.000000 |
293.50 |
2.4667802 |
2.396373 |
1.0293807 |
| d11/heterozyg+siCtrl |
exp1 |
d11/heterozyg |
siCtrl |
exp1 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
X30nM |
1532 |
1.477121 |
1488.00 |
1.0295699 |
1.159350 |
0.8880580 |
| d11/heterozyg+siALC1 |
exp1 |
d11/heterozyg |
siALC1 |
exp1 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
X30nM |
820 |
1.477121 |
860.00 |
0.9534884 |
1.612312 |
0.5913795 |
| d11/d11+siCtrl |
exp1 |
d11/d11 |
siCtrl |
exp1 siCtrl d11/d11 |
siCtrl d11/d11 |
X30nM |
632 |
1.477121 |
519.75 |
1.2159692 |
2.142651 |
0.5675068 |
| d11/d11+siALC1 |
exp1 |
d11/d11 |
siALC1 |
exp1 siALC1 d11/d11 |
siALC1 d11/d11 |
X30nM |
296 |
1.477121 |
283.25 |
1.0450132 |
2.396373 |
0.4360812 |
| d11/heterozyg+siCtrl |
exp2 |
d11/heterozyg |
siCtrl |
exp2 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
X30nM |
1196 |
1.477121 |
1109.00 |
1.0784491 |
1.159350 |
0.9302188 |
| d11/heterozyg+siALC1 |
exp2 |
d11/heterozyg |
siALC1 |
exp2 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
X30nM |
1048 |
1.477121 |
1111.00 |
0.9432943 |
1.612312 |
0.5850569 |
| d11/d11+siCtrl |
exp2 |
d11/d11 |
siCtrl |
exp2 siCtrl d11/d11 |
siCtrl d11/d11 |
X30nM |
360 |
1.477121 |
406.25 |
0.8861538 |
2.142651 |
0.4135781 |
| d11/d11+siALC1 |
exp2 |
d11/d11 |
siALC1 |
exp2 siALC1 d11/d11 |
siALC1 d11/d11 |
X30nM |
336 |
1.477121 |
319.50 |
1.0516432 |
2.396373 |
0.4388479 |
| d11/heterozyg+siCtrl |
exp3 |
d11/heterozyg |
siCtrl |
exp3 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
X30nM |
1088 |
1.477121 |
1019.75 |
1.0669282 |
1.159350 |
0.9202815 |
| d11/heterozyg+siALC1 |
exp3 |
d11/heterozyg |
siALC1 |
exp3 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
X30nM |
832 |
1.477121 |
756.25 |
1.1001653 |
1.612312 |
0.6823526 |
| d11/d11+siCtrl |
exp3 |
d11/d11 |
siCtrl |
exp3 siCtrl d11/d11 |
siCtrl d11/d11 |
X30nM |
580 |
1.477121 |
474.50 |
1.2223393 |
2.142651 |
0.5704797 |
| d11/d11+siALC1 |
exp3 |
d11/d11 |
siALC1 |
exp3 siALC1 d11/d11 |
siALC1 d11/d11 |
X30nM |
320 |
1.477121 |
293.50 |
1.0902896 |
2.396373 |
0.4549749 |
| d11/heterozyg+siCtrl |
exp1 |
d11/heterozyg |
siCtrl |
exp1 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
X300nM |
1328 |
2.477121 |
1488.00 |
0.8924731 |
1.159350 |
0.7698048 |
| d11/heterozyg+siALC1 |
exp1 |
d11/heterozyg |
siALC1 |
exp1 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
X300nM |
772 |
2.477121 |
860.00 |
0.8976744 |
1.612312 |
0.5567622 |
| d11/d11+siCtrl |
exp1 |
d11/d11 |
siCtrl |
exp1 siCtrl d11/d11 |
siCtrl d11/d11 |
X300nM |
356 |
2.477121 |
519.75 |
0.6849447 |
2.142651 |
0.3196715 |
| d11/d11+siALC1 |
exp1 |
d11/d11 |
siALC1 |
exp1 siALC1 d11/d11 |
siALC1 d11/d11 |
X300nM |
220 |
2.477121 |
283.25 |
0.7766990 |
2.396373 |
0.3241144 |
| d11/heterozyg+siCtrl |
exp2 |
d11/heterozyg |
siCtrl |
exp2 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
X300nM |
1036 |
2.477121 |
1109.00 |
0.9341749 |
1.159350 |
0.8057748 |
| d11/heterozyg+siALC1 |
exp2 |
d11/heterozyg |
siALC1 |
exp2 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
X300nM |
840 |
2.477121 |
1111.00 |
0.7560756 |
1.612312 |
0.4689387 |
| d11/d11+siCtrl |
exp2 |
d11/d11 |
siCtrl |
exp2 siCtrl d11/d11 |
siCtrl d11/d11 |
X300nM |
304 |
2.477121 |
406.25 |
0.7483077 |
2.142651 |
0.3492438 |
| d11/d11+siALC1 |
exp2 |
d11/d11 |
siALC1 |
exp2 siALC1 d11/d11 |
siALC1 d11/d11 |
X300nM |
106 |
2.477121 |
319.50 |
0.3317684 |
2.396373 |
0.1384461 |
| d11/heterozyg+siCtrl |
exp3 |
d11/heterozyg |
siCtrl |
exp3 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
X300nM |
940 |
2.477121 |
1019.75 |
0.9217946 |
1.159350 |
0.7950961 |
| d11/heterozyg+siALC1 |
exp3 |
d11/heterozyg |
siALC1 |
exp3 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
X300nM |
605 |
2.477121 |
756.25 |
0.8000000 |
1.612312 |
0.4961818 |
| d11/d11+siCtrl |
exp3 |
d11/d11 |
siCtrl |
exp3 siCtrl d11/d11 |
siCtrl d11/d11 |
X300nM |
324 |
2.477121 |
474.50 |
0.6828240 |
2.142651 |
0.3186818 |
| d11/d11+siALC1 |
exp3 |
d11/d11 |
siALC1 |
exp3 siALC1 d11/d11 |
siALC1 d11/d11 |
X300nM |
124 |
2.477121 |
293.50 |
0.4224872 |
2.396373 |
0.1763028 |
| d11/heterozyg+siCtrl |
exp1 |
d11/heterozyg |
siCtrl |
exp1 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
X3000nM |
1276 |
3.477121 |
1488.00 |
0.8575269 |
1.159350 |
0.7396619 |
| d11/heterozyg+siALC1 |
exp1 |
d11/heterozyg |
siALC1 |
exp1 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
X3000nM |
560 |
3.477121 |
860.00 |
0.6511628 |
1.612312 |
0.4038689 |
| d11/d11+siCtrl |
exp1 |
d11/d11 |
siCtrl |
exp1 siCtrl d11/d11 |
siCtrl d11/d11 |
X3000nM |
51 |
3.477121 |
519.75 |
0.0981241 |
2.142651 |
0.0457956 |
| d11/d11+siALC1 |
exp1 |
d11/d11 |
siALC1 |
exp1 siALC1 d11/d11 |
siALC1 d11/d11 |
X3000nM |
17 |
3.477121 |
283.25 |
0.0600177 |
2.396373 |
0.0250452 |
| d11/heterozyg+siCtrl |
exp2 |
d11/heterozyg |
siCtrl |
exp2 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
X3000nM |
964 |
3.477121 |
1109.00 |
0.8692516 |
1.159350 |
0.7497750 |
| d11/heterozyg+siALC1 |
exp2 |
d11/heterozyg |
siALC1 |
exp2 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
X3000nM |
656 |
3.477121 |
1111.00 |
0.5904590 |
1.612312 |
0.3662188 |
| d11/d11+siCtrl |
exp2 |
d11/d11 |
siCtrl |
exp2 siCtrl d11/d11 |
siCtrl d11/d11 |
X3000nM |
5 |
3.477121 |
406.25 |
0.0123077 |
2.142651 |
0.0057441 |
| d11/d11+siALC1 |
exp2 |
d11/d11 |
siALC1 |
exp2 siALC1 d11/d11 |
siALC1 d11/d11 |
X3000nM |
4 |
3.477121 |
319.50 |
0.0125196 |
2.396373 |
0.0052244 |
| d11/heterozyg+siCtrl |
exp3 |
d11/heterozyg |
siCtrl |
exp3 siCtrl d11/heterozyg |
siCtrl d11/heterozyg |
X3000nM |
889 |
3.477121 |
1019.75 |
0.8717823 |
1.159350 |
0.7519579 |
| d11/heterozyg+siALC1 |
exp3 |
d11/heterozyg |
siALC1 |
exp3 siALC1 d11/heterozyg |
siALC1 d11/heterozyg |
X3000nM |
356 |
3.477121 |
756.25 |
0.4707438 |
1.612312 |
0.2919682 |
| d11/d11+siCtrl |
exp3 |
d11/d11 |
siCtrl |
exp3 siCtrl d11/d11 |
siCtrl d11/d11 |
X3000nM |
10 |
3.477121 |
474.50 |
0.0210748 |
2.142651 |
0.0098359 |
| d11/d11+siALC1 |
exp3 |
d11/d11 |
siALC1 |
exp3 siALC1 d11/d11 |
siALC1 d11/d11 |
X3000nM |
6 |
3.477121 |
293.50 |
0.0204429 |
2.396373 |
0.0085308 |
Plot Data
library(ggplot2)
# raw data
ggplot(dataset, aes(x=Olaparib, y=Counts)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE, aes(colour=BRCA)) +
geom_point(aes(colour=BRCA, shape=Experiment), size=2) +
facet_grid(. ~ siRNA) +
xlab(label = "Olaparib (log10 nM)") +
scale_shape_manual(values=15:20) +
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Linear
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=BRCA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=BRCA), size=2) +
geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
facet_grid(. ~ siRNA) +
xlab(label = "Olaparib (log10 nM)") +
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Linear
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=BRCA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=BRCA), size=2) +
geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
facet_grid(. ~ siRNA) +
xlab(label = "Olaparib (log10 nM)") +
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Quadratic
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=BRCA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=BRCA), size=2) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
facet_grid(. ~ siRNA) +
xlab(label = "Olaparib (log10 nM)")+
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Quadratic
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=BRCA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=BRCA), size=2) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
facet_grid(. ~ siRNA) +
xlab(label = "Olaparib (log10 nM)") +
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Cubic
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=BRCA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=BRCA), size=2) +
geom_smooth(method=lm, formula = y ~ poly(x,3), se=FALSE) +
facet_grid(. ~ siRNA) +
xlab(label = "Olaparib (log10 nM)")+
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Cubic
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=BRCA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=BRCA), size=2) +
geom_smooth(method=lm, formula = y ~ poly(x,3), se=FALSE) +
facet_grid(. ~ siRNA) +
xlab(label = "Olaparib (log10 nM)") +
scale_color_manual(values=c("#000000","#FF0000"))

library(Cairo)
cairo_pdf("Figure5A.pdf", width = 6, height = 4, family = "Arial")
ggplot(dataset, aes(x=Olaparib, y=NormCounts2)) +
theme_bw() +
theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(),
axis.line = element_line(colour = "black"), text = element_text(size=14),
panel.border = element_blank(), panel.background = element_blank()) +
geom_point(aes(colour = BRCA, shape = siRNA), size=1.75) +
geom_smooth(method=lm, formula = y ~ poly(x,3), se=TRUE,
aes(group = GSID,colour = BRCA, linetype = siRNA), fill='#DDDDDD', size=0.5) +
xlab(label = "Olaparib (log10 nM)") +
ylab(label = "Normalized Counts") +
scale_color_manual(values=c("#000000","#FF0000")) +
guides(linetype = guide_legend(override.aes= list(color = "#555555")))
dev.off()
## quartz_off_screen
## 2
Models
library(MASS)
library(DHARMa)
library(lme4)
library(lmerTest)
library(bbmle)
Linear formula
fit1 <- lm(Counts ~ Experiment + Olaparib*BRCA*siRNA, data = dataset)
print(summary(fit1))
##
## Call:
## lm(formula = Counts ~ Experiment + Olaparib * BRCA * siRNA, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -259.05 -80.68 -19.32 83.93 475.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1477.415 84.243 17.537 < 2e-16 ***
## Experimentexp2 -51.312 54.445 -0.942 0.351912
## Experimentexp3 -151.750 54.445 -2.787 0.008255 **
## Olaparib -109.883 34.602 -3.176 0.002964 **
## BRCAd11/d11 -434.626 110.534 -3.932 0.000345 ***
## siRNAsiALC1 -1.518 110.534 -0.014 0.989114
## Olaparib:BRCAd11/d11 -163.698 48.935 -3.345 0.001860 **
## Olaparib:siRNAsiALC1 -158.777 48.935 -3.245 0.002455 **
## BRCAd11/d11:siRNAsiALC1 -295.359 156.318 -1.889 0.066476 .
## Olaparib:BRCAd11/d11:siRNAsiALC1 228.101 69.205 3.296 0.002131 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 154 on 38 degrees of freedom
## Multiple R-squared: 0.9197, Adjusted R-squared: 0.9007
## F-statistic: 48.38 on 9 and 38 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit1))
## AIC: 630.5489
simres <- simulateResiduals(fittedModel = fit1)
plot(simres)

fit2 <- lm(NormCounts ~ Olaparib*BRCA*siRNA, data = dataset)
print(summary(fit2))
##
## Call:
## lm(formula = NormCounts ~ Olaparib * BRCA * siRNA, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33845 -0.05228 -0.00645 0.07065 0.34049
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.16514 0.07281 16.003 < 2e-16 ***
## Olaparib -0.08889 0.03223 -2.758 0.008730 **
## BRCAd11/d11 0.93086 0.10296 9.041 3.26e-11 ***
## siRNAsiALC1 0.38217 0.10296 3.712 0.000627 ***
## Olaparib:BRCAd11/d11 -0.50104 0.04558 -10.992 1.18e-13 ***
## Olaparib:siRNAsiALC1 -0.20571 0.04558 -4.513 5.51e-05 ***
## BRCAd11/d11:siRNAsiALC1 -0.21460 0.14561 -1.474 0.148372
## Olaparib:BRCAd11/d11:siRNAsiALC1 0.11551 0.06446 1.792 0.080729 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1434 on 40 degrees of freedom
## Multiple R-squared: 0.9561, Adjusted R-squared: 0.9484
## F-statistic: 124.5 on 7 and 40 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit2))
## AIC: -40.94425
simres <- simulateResiduals(fittedModel = fit2)
plot(simres)

fit3 <- lm(NormCounts2 ~ Olaparib*BRCA*siRNA, data = dataset)
print(summary(fit3))
##
## Call:
## lm(formula = NormCounts2 ~ Olaparib * BRCA * siRNA, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.157957 -0.038740 -0.003856 0.047553 0.142085
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.00500 0.03461 29.038 < 2e-16 ***
## Olaparib -0.07667 0.01532 -5.004 1.17e-05 ***
## BRCAd11/d11 -0.02677 0.04894 -0.547 0.58749
## siRNAsiALC1 -0.04531 0.04894 -0.926 0.36015
## Olaparib:BRCAd11/d11 -0.19866 0.02167 -9.168 2.22e-11 ***
## Olaparib:siRNAsiALC1 -0.10605 0.02167 -4.894 1.66e-05 ***
## BRCAd11/d11:siRNAsiALC1 0.01167 0.06922 0.169 0.86702
## Olaparib:BRCAd11/d11:siRNAsiALC1 0.09756 0.03064 3.184 0.00282 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06819 on 40 degrees of freedom
## Multiple R-squared: 0.9662, Adjusted R-squared: 0.9603
## F-statistic: 163.4 on 7 and 40 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit3))
## AIC: -112.3374
simres <- simulateResiduals(fittedModel = fit3)
plot(simres)

fit4 <- lmer(Counts ~ Olaparib*BRCA*siRNA + (1|UID), data = dataset)
print(summary(fit4))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ Olaparib * BRCA * siRNA + (1 | UID)
## Data: dataset
##
## REML criterion at convergence: 520.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6072 -0.5520 0.0208 0.3678 3.1814
##
## Random effects:
## Groups Name Variance Std.Dev.
## UID (Intercept) 22312 149.37
## Residual 9446 97.19
## Number of obs: 48, groups: UID, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1409.728 99.350 11.409 14.189 1.34e-08
## Olaparib -109.883 21.838 32.000 -5.032 1.81e-05
## BRCAd11/d11 -434.626 140.503 11.409 -3.093 0.00982
## siRNAsiALC1 -1.518 140.503 11.409 -0.011 0.99157
## Olaparib:BRCAd11/d11 -163.698 30.884 32.000 -5.300 8.29e-06
## Olaparib:siRNAsiALC1 -158.777 30.884 32.000 -5.141 1.32e-05
## BRCAd11/d11:siRNAsiALC1 -295.359 198.701 11.409 -1.486 0.16427
## Olaparib:BRCAd11/d11:siRNAsiALC1 228.101 43.676 32.000 5.223 1.04e-05
##
## (Intercept) ***
## Olaparib ***
## BRCAd11/d11 **
## siRNAsiALC1
## Olaparib:BRCAd11/d11 ***
## Olaparib:siRNAsiALC1 ***
## BRCAd11/d11:siRNAsiALC1
## Olaparib:BRCAd11/d11:siRNAsiALC1 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Olaprb BRCAd11/11 sRNAAL Ol:BRCA11/11 O:RNAA BRCA11/11:
## Olaparib -0.408
## BRCAd11/d11 -0.707 0.289
## siRNAsiALC1 -0.707 0.289 0.500
## Ol:BRCA11/11 0.289 -0.707 -0.408 -0.204
## Olp:RNAALC1 0.289 -0.707 -0.204 -0.408 0.500
## BRCA11/11:R 0.500 -0.204 -0.707 -0.707 0.289 0.289
## O:BRCA11/11: -0.204 0.500 0.289 0.289 -0.707 -0.707 -0.408
cat("AIC: ", AIC(fit4))
## AIC: 540.303
simres <- simulateResiduals(fittedModel = fit4)
plot(simres)

Quadratic formula
fit5 <- lm(Counts ~ Experiment + poly(Olaparib,2)*BRCA*siRNA, data = dataset)
print(summary(fit5))
##
## Call:
## lm(formula = Counts ~ Experiment + poly(Olaparib, 2) * BRCA *
## siRNA, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -244.21 -74.58 -10.13 84.73 419.11
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1273.27 55.31 23.019 < 2e-16
## Experimentexp2 -51.31 55.31 -0.928 0.36012
## Experimentexp3 -151.75 55.31 -2.743 0.00963
## poly(Olaparib, 2)1 -978.05 312.90 -3.126 0.00362
## poly(Olaparib, 2)2 21.63 312.90 0.069 0.94530
## BRCAd11/d11 -738.75 63.87 -11.566 2.48e-13
## siRNAsiALC1 -296.50 63.87 -4.642 4.97e-05
## poly(Olaparib, 2)1:BRCAd11/d11 -1457.05 442.51 -3.293 0.00232
## poly(Olaparib, 2)2:BRCAd11/d11 58.32 442.51 0.132 0.89592
## poly(Olaparib, 2)1:siRNAsiALC1 -1413.25 442.51 -3.194 0.00302
## poly(Olaparib, 2)2:siRNAsiALC1 407.20 442.51 0.920 0.36394
## BRCAd11/d11:siRNAsiALC1 128.42 90.33 1.422 0.16422
## poly(Olaparib, 2)1:BRCAd11/d11:siRNAsiALC1 2030.29 625.80 3.244 0.00264
## poly(Olaparib, 2)2:BRCAd11/d11:siRNAsiALC1 -195.54 625.80 -0.312 0.75659
##
## (Intercept) ***
## Experimentexp2
## Experimentexp3 **
## poly(Olaparib, 2)1 **
## poly(Olaparib, 2)2
## BRCAd11/d11 ***
## siRNAsiALC1 ***
## poly(Olaparib, 2)1:BRCAd11/d11 **
## poly(Olaparib, 2)2:BRCAd11/d11
## poly(Olaparib, 2)1:siRNAsiALC1 **
## poly(Olaparib, 2)2:siRNAsiALC1
## BRCAd11/d11:siRNAsiALC1
## poly(Olaparib, 2)1:BRCAd11/d11:siRNAsiALC1 **
## poly(Olaparib, 2)2:BRCAd11/d11:siRNAsiALC1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 156.4 on 34 degrees of freedom
## Multiple R-squared: 0.9259, Adjusted R-squared: 0.8975
## F-statistic: 32.67 on 13 and 34 DF, p-value: 2.165e-15
cat("AIC: ", AIC(fit5))
## AIC: 634.7283
simres <- simulateResiduals(fittedModel = fit5)
plot(simres)

fit6 <- lm(NormCounts ~ poly(Olaparib,2)*BRCA*siRNA, data = dataset)
print(summary(fit6))
##
## Call:
## lm(formula = NormCounts ~ poly(Olaparib, 2) * BRCA * siRNA, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.301786 -0.050527 -0.001414 0.047162 0.306589
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.000e+00 3.537e-02 28.271
## poly(Olaparib, 2)1 -7.912e-01 2.451e-01 -3.229
## poly(Olaparib, 2)2 -1.202e-03 2.451e-01 -0.005
## BRCAd11/d11 3.668e-16 5.002e-02 0.000
## siRNAsiALC1 3.621e-16 5.002e-02 0.000
## poly(Olaparib, 2)1:BRCAd11/d11 -4.460e+00 3.466e-01 -12.868
## poly(Olaparib, 2)2:BRCAd11/d11 2.154e-01 3.466e-01 0.621
## poly(Olaparib, 2)1:siRNAsiALC1 -1.831e+00 3.466e-01 -5.283
## poly(Olaparib, 2)2:siRNAsiALC1 4.240e-01 3.466e-01 1.224
## BRCAd11/d11:siRNAsiALC1 -3.308e-16 7.074e-02 0.000
## poly(Olaparib, 2)1:BRCAd11/d11:siRNAsiALC1 1.028e+00 4.901e-01 2.098
## poly(Olaparib, 2)2:BRCAd11/d11:siRNAsiALC1 3.135e-01 4.901e-01 0.640
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(Olaparib, 2)1 0.00265 **
## poly(Olaparib, 2)2 0.99611
## BRCAd11/d11 1.00000
## siRNAsiALC1 1.00000
## poly(Olaparib, 2)1:BRCAd11/d11 4.94e-15 ***
## poly(Olaparib, 2)2:BRCAd11/d11 0.53825
## poly(Olaparib, 2)1:siRNAsiALC1 6.31e-06 ***
## poly(Olaparib, 2)2:siRNAsiALC1 0.22907
## BRCAd11/d11:siRNAsiALC1 1.00000
## poly(Olaparib, 2)1:BRCAd11/d11:siRNAsiALC1 0.04302 *
## poly(Olaparib, 2)2:BRCAd11/d11:siRNAsiALC1 0.52649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1225 on 36 degrees of freedom
## Multiple R-squared: 0.9712, Adjusted R-squared: 0.9624
## F-statistic: 110.3 on 11 and 36 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit6))
## AIC: -53.13147
simres <- simulateResiduals(fittedModel = fit6)
plot(simres)

fit7 <- lm(NormCounts2 ~ poly(Olaparib,2)*BRCA*siRNA, data = dataset)
print(summary(fit7))
##
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 2) * BRCA * siRNA,
## data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.140847 -0.024239 -0.000486 0.032959 0.127939
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.862552 0.017137 50.332
## poly(Olaparib, 2)1 -0.682445 0.118731 -5.748
## poly(Olaparib, 2)2 -0.001037 0.118731 -0.009
## BRCAd11/d11 -0.395841 0.024236 -16.333
## siRNAsiALC1 -0.242325 0.024236 -9.999
## poly(Olaparib, 2)1:BRCAd11/d11 -1.768219 0.167911 -10.531
## poly(Olaparib, 2)2:BRCAd11/d11 0.100986 0.167911 0.601
## poly(Olaparib, 2)1:siRNAsiALC1 -0.943899 0.167911 -5.621
## poly(Olaparib, 2)2:siRNAsiALC1 0.263296 0.167911 1.568
## BRCAd11/d11:siRNAsiALC1 0.192911 0.034275 5.628
## poly(Olaparib, 2)1:BRCAd11/d11:siRNAsiALC1 0.868342 0.237461 3.657
## poly(Olaparib, 2)2:BRCAd11/d11:siRNAsiALC1 0.033889 0.237461 0.143
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(Olaparib, 2)1 1.51e-06 ***
## poly(Olaparib, 2)2 0.99308
## BRCAd11/d11 < 2e-16 ***
## siRNAsiALC1 6.24e-12 ***
## poly(Olaparib, 2)1:BRCAd11/d11 1.53e-12 ***
## poly(Olaparib, 2)2:BRCAd11/d11 0.55132
## poly(Olaparib, 2)1:siRNAsiALC1 2.23e-06 ***
## poly(Olaparib, 2)2:siRNAsiALC1 0.12561
## BRCAd11/d11:siRNAsiALC1 2.18e-06 ***
## poly(Olaparib, 2)1:BRCAd11/d11:siRNAsiALC1 0.00081 ***
## poly(Olaparib, 2)2:BRCAd11/d11:siRNAsiALC1 0.88731
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05937 on 36 degrees of freedom
## Multiple R-squared: 0.9769, Adjusted R-squared: 0.9699
## F-statistic: 138.7 on 11 and 36 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit7))
## AIC: -122.6989
simres <- simulateResiduals(fittedModel = fit7)
plot(simres)

fit8 <- lmer(Counts ~ poly(Olaparib,2)*BRCA*siRNA + (1|UID), data = dataset)
print(summary(fit8))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Olaparib, 2) * BRCA * siRNA + (1 | UID)
## Data: dataset
##
## REML criterion at convergence: 445.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.55381 -0.43817 -0.02146 0.30996 2.74542
##
## Random effects:
## Groups Name Variance Std.Dev.
## UID (Intercept) 22590 150.30
## Residual 8333 91.28
## Number of obs: 48, groups: UID, 12
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1205.58 90.69 8.00 13.294
## poly(Olaparib, 2)1 -978.05 182.57 28.00 -5.357
## poly(Olaparib, 2)2 21.62 182.57 28.00 0.118
## BRCAd11/d11 -738.75 128.25 8.00 -5.760
## siRNAsiALC1 -296.50 128.25 8.00 -2.312
## poly(Olaparib, 2)1:BRCAd11/d11 -1457.05 258.19 28.00 -5.643
## poly(Olaparib, 2)2:BRCAd11/d11 58.32 258.19 28.00 0.226
## poly(Olaparib, 2)1:siRNAsiALC1 -1413.25 258.19 28.00 -5.474
## poly(Olaparib, 2)2:siRNAsiALC1 407.20 258.19 28.00 1.577
## BRCAd11/d11:siRNAsiALC1 128.42 181.38 8.00 0.708
## poly(Olaparib, 2)1:BRCAd11/d11:siRNAsiALC1 2030.29 365.13 28.00 5.560
## poly(Olaparib, 2)2:BRCAd11/d11:siRNAsiALC1 -195.54 365.13 28.00 -0.536
## Pr(>|t|)
## (Intercept) 9.79e-07 ***
## poly(Olaparib, 2)1 1.05e-05 ***
## poly(Olaparib, 2)2 0.906555
## BRCAd11/d11 0.000424 ***
## siRNAsiALC1 0.049547 *
## poly(Olaparib, 2)1:BRCAd11/d11 4.80e-06 ***
## poly(Olaparib, 2)2:BRCAd11/d11 0.822934
## poly(Olaparib, 2)1:siRNAsiALC1 7.61e-06 ***
## poly(Olaparib, 2)2:siRNAsiALC1 0.125992
## BRCAd11/d11:siRNAsiALC1 0.499046
## poly(Olaparib, 2)1:BRCAd11/d11:siRNAsiALC1 6.01e-06 ***
## poly(Olaparib, 2)2:BRCAd11/d11:siRNAsiALC1 0.596506
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pl(O,2)1 pl(O,2)2 BRCAd11/11 sRNAAL
## ply(Olp,2)1 0.000
## ply(Olp,2)2 0.000 0.000
## BRCAd11/d11 -0.707 0.000 0.000
## siRNAsiALC1 -0.707 0.000 0.000 0.500
## pl(O,2)1:BRCA11/11 0.000 -0.707 0.000 0.000 0.000
## pl(O,2)2:BRCA11/11 0.000 0.000 -0.707 0.000 0.000
## p(O,2)1:RNA 0.000 -0.707 0.000 0.000 0.000
## p(O,2)2:RNA 0.000 0.000 -0.707 0.000 0.000
## BRCA11/11:R 0.500 0.000 0.000 -0.707 -0.707
## p(O,2)1:BRCA11/11: 0.000 0.500 0.000 0.000 0.000
## p(O,2)2:BRCA11/11: 0.000 0.000 0.500 0.000 0.000
## pl(O,2)1:BRCA11/11 pl(O,2)2:BRCA11/11 p(O,2)1:R p(O,2)2:R
## ply(Olp,2)1
## ply(Olp,2)2
## BRCAd11/d11
## siRNAsiALC1
## pl(O,2)1:BRCA11/11
## pl(O,2)2:BRCA11/11 0.000
## p(O,2)1:RNA 0.500 0.000
## p(O,2)2:RNA 0.000 0.500 0.000
## BRCA11/11:R 0.000 0.000 0.000 0.000
## p(O,2)1:BRCA11/11: -0.707 0.000 -0.707 0.000
## p(O,2)2:BRCA11/11: 0.000 -0.707 0.000 -0.707
## BRCA11/11: p(O,2)1:BRCA11/11:
## ply(Olp,2)1
## ply(Olp,2)2
## BRCAd11/d11
## siRNAsiALC1
## pl(O,2)1:BRCA11/11
## pl(O,2)2:BRCA11/11
## p(O,2)1:RNA
## p(O,2)2:RNA
## BRCA11/11:R
## p(O,2)1:BRCA11/11: 0.000
## p(O,2)2:BRCA11/11: 0.000 0.000
cat("AIC: ", AIC(fit8))
## AIC: 473.7934
simres <- simulateResiduals(fittedModel = fit8)
plot(simres)

Cubic formula
fit9 <- lm(Counts ~ Experiment + poly(Olaparib,3)*BRCA*siRNA, data = dataset)
print(summary(fit9))
##
## Call:
## lm(formula = Counts ~ Experiment + poly(Olaparib, 3) * BRCA *
## siRNA, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -253.02 -82.27 -32.53 81.95 410.29
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1273.27 57.91 21.988 < 2e-16
## Experimentexp2 -51.31 57.91 -0.886 0.382615
## Experimentexp3 -151.75 57.91 -2.621 0.013647
## poly(Olaparib, 3)1 -978.05 327.58 -2.986 0.005588
## poly(Olaparib, 3)2 21.63 327.58 0.066 0.947803
## poly(Olaparib, 3)3 159.70 327.58 0.488 0.629439
## BRCAd11/d11 -738.75 66.87 -11.048 4.27e-12
## siRNAsiALC1 -296.50 66.87 -4.434 0.000114
## poly(Olaparib, 3)1:BRCAd11/d11 -1457.05 463.27 -3.145 0.003729
## poly(Olaparib, 3)2:BRCAd11/d11 58.32 463.27 0.126 0.900660
## poly(Olaparib, 3)3:BRCAd11/d11 -347.75 463.27 -0.751 0.458712
## poly(Olaparib, 3)1:siRNAsiALC1 -1413.25 463.27 -3.051 0.004744
## poly(Olaparib, 3)2:siRNAsiALC1 407.20 463.27 0.879 0.386398
## poly(Olaparib, 3)3:siRNAsiALC1 -374.03 463.27 -0.807 0.425809
## BRCAd11/d11:siRNAsiALC1 128.42 94.56 1.358 0.184596
## poly(Olaparib, 3)1:BRCAd11/d11:siRNAsiALC1 2030.29 655.16 3.099 0.004196
## poly(Olaparib, 3)2:BRCAd11/d11:siRNAsiALC1 -195.54 655.16 -0.298 0.767405
## poly(Olaparib, 3)3:BRCAd11/d11:siRNAsiALC1 509.78 655.16 0.778 0.442606
##
## (Intercept) ***
## Experimentexp2
## Experimentexp3 *
## poly(Olaparib, 3)1 **
## poly(Olaparib, 3)2
## poly(Olaparib, 3)3
## BRCAd11/d11 ***
## siRNAsiALC1 ***
## poly(Olaparib, 3)1:BRCAd11/d11 **
## poly(Olaparib, 3)2:BRCAd11/d11
## poly(Olaparib, 3)3:BRCAd11/d11
## poly(Olaparib, 3)1:siRNAsiALC1 **
## poly(Olaparib, 3)2:siRNAsiALC1
## poly(Olaparib, 3)3:siRNAsiALC1
## BRCAd11/d11:siRNAsiALC1
## poly(Olaparib, 3)1:BRCAd11/d11:siRNAsiALC1 **
## poly(Olaparib, 3)2:BRCAd11/d11:siRNAsiALC1
## poly(Olaparib, 3)3:BRCAd11/d11:siRNAsiALC1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 163.8 on 30 degrees of freedom
## Multiple R-squared: 0.9283, Adjusted R-squared: 0.8877
## F-statistic: 22.85 on 17 and 30 DF, p-value: 1.178e-12
cat("AIC: ", AIC(fit9))
## AIC: 641.1222
simres <- simulateResiduals(fittedModel = fit9)
plot(simres)

fit10 <- lm(NormCounts ~ poly(Olaparib,3)*BRCA*siRNA, data = dataset)
print(summary(fit10))
##
## Call:
## lm(formula = NormCounts ~ poly(Olaparib, 3) * BRCA * siRNA, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.278103 -0.033952 -0.009605 0.045784 0.266381
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.000e+00 3.471e-02 28.806
## poly(Olaparib, 3)1 -7.912e-01 2.405e-01 -3.290
## poly(Olaparib, 3)2 -1.202e-03 2.405e-01 -0.005
## poly(Olaparib, 3)3 1.370e-01 2.405e-01 0.570
## BRCAd11/d11 5.440e-16 4.909e-02 0.000
## siRNAsiALC1 5.092e-16 4.909e-02 0.000
## poly(Olaparib, 3)1:BRCAd11/d11 -4.460e+00 3.401e-01 -13.112
## poly(Olaparib, 3)2:BRCAd11/d11 2.154e-01 3.401e-01 0.633
## poly(Olaparib, 3)3:BRCAd11/d11 -5.875e-01 3.401e-01 -1.727
## poly(Olaparib, 3)1:siRNAsiALC1 -1.831e+00 3.401e-01 -5.383
## poly(Olaparib, 3)2:siRNAsiALC1 4.240e-01 3.401e-01 1.247
## poly(Olaparib, 3)3:siRNAsiALC1 -3.674e-01 3.401e-01 -1.080
## BRCAd11/d11:siRNAsiALC1 -3.292e-16 6.943e-02 0.000
## poly(Olaparib, 3)1:BRCAd11/d11:siRNAsiALC1 1.028e+00 4.810e-01 2.137
## poly(Olaparib, 3)2:BRCAd11/d11:siRNAsiALC1 3.135e-01 4.810e-01 0.652
## poly(Olaparib, 3)3:BRCAd11/d11:siRNAsiALC1 6.275e-01 4.810e-01 1.305
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(Olaparib, 3)1 0.00245 **
## poly(Olaparib, 3)2 0.99604
## poly(Olaparib, 3)3 0.57284
## BRCAd11/d11 1.00000
## siRNAsiALC1 1.00000
## poly(Olaparib, 3)1:BRCAd11/d11 2.05e-14 ***
## poly(Olaparib, 3)2:BRCAd11/d11 0.53113
## poly(Olaparib, 3)3:BRCAd11/d11 0.09375 .
## poly(Olaparib, 3)1:siRNAsiALC1 6.52e-06 ***
## poly(Olaparib, 3)2:siRNAsiALC1 0.22155
## poly(Olaparib, 3)3:siRNAsiALC1 0.28820
## BRCAd11/d11:siRNAsiALC1 1.00000
## poly(Olaparib, 3)1:BRCAd11/d11:siRNAsiALC1 0.04030 *
## poly(Olaparib, 3)2:BRCAd11/d11:siRNAsiALC1 0.51925
## poly(Olaparib, 3)3:BRCAd11/d11:siRNAsiALC1 0.20137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1203 on 32 degrees of freedom
## Multiple R-squared: 0.9753, Adjusted R-squared: 0.9638
## F-statistic: 84.32 on 15 and 32 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit10))
## AIC: -52.58635
simres <- simulateResiduals(fittedModel = fit10)
plot(simres)

fit11 <- lm(NormCounts2 ~ poly(Olaparib,3)*BRCA*siRNA, data = dataset)
print(summary(fit11))
##
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * BRCA * siRNA,
## data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.116052 -0.025650 -0.004428 0.021368 0.111160
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.862552 0.016587 52.002
## poly(Olaparib, 3)1 -0.682445 0.114918 -5.939
## poly(Olaparib, 3)2 -0.001037 0.114918 -0.009
## poly(Olaparib, 3)3 0.118190 0.114918 1.028
## BRCAd11/d11 -0.395841 0.023458 -16.875
## siRNAsiALC1 -0.242325 0.023458 -10.330
## poly(Olaparib, 3)1:BRCAd11/d11 -1.768219 0.162519 -10.880
## poly(Olaparib, 3)2:BRCAd11/d11 0.100986 0.162519 0.621
## poly(Olaparib, 3)3:BRCAd11/d11 -0.328440 0.162519 -2.021
## poly(Olaparib, 3)1:siRNAsiALC1 -0.943899 0.162519 -5.808
## poly(Olaparib, 3)2:siRNAsiALC1 0.263296 0.162519 1.620
## poly(Olaparib, 3)3:siRNAsiALC1 -0.261047 0.162519 -1.606
## BRCAd11/d11:siRNAsiALC1 0.192911 0.033174 5.815
## poly(Olaparib, 3)1:BRCAd11/d11:siRNAsiALC1 0.868342 0.229837 3.778
## poly(Olaparib, 3)2:BRCAd11/d11:siRNAsiALC1 0.033889 0.229837 0.147
## poly(Olaparib, 3)3:BRCAd11/d11:siRNAsiALC1 0.391861 0.229837 1.705
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(Olaparib, 3)1 1.30e-06 ***
## poly(Olaparib, 3)2 0.99286
## poly(Olaparib, 3)3 0.31144
## BRCAd11/d11 < 2e-16 ***
## siRNAsiALC1 1.02e-11 ***
## poly(Olaparib, 3)1:BRCAd11/d11 2.77e-12 ***
## poly(Olaparib, 3)2:BRCAd11/d11 0.53875
## poly(Olaparib, 3)3:BRCAd11/d11 0.05172 .
## poly(Olaparib, 3)1:siRNAsiALC1 1.89e-06 ***
## poly(Olaparib, 3)2:siRNAsiALC1 0.11503
## poly(Olaparib, 3)3:siRNAsiALC1 0.11804
## BRCAd11/d11:siRNAsiALC1 1.85e-06 ***
## poly(Olaparib, 3)1:BRCAd11/d11:siRNAsiALC1 0.00065 ***
## poly(Olaparib, 3)2:BRCAd11/d11:siRNAsiALC1 0.88370
## poly(Olaparib, 3)3:BRCAd11/d11:siRNAsiALC1 0.09789 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05746 on 32 degrees of freedom
## Multiple R-squared: 0.9808, Adjusted R-squared: 0.9718
## F-statistic: 109 on 15 and 32 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit11))
## AIC: -123.4856
simres <- simulateResiduals(fittedModel = fit11)
plot(simres)

fit12 <- lmer(Counts ~ poly(Olaparib,3)*BRCA*siRNA + (1|UID), data = dataset)
print(summary(fit12))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Olaparib, 3) * BRCA * siRNA + (1 | UID)
## Data: dataset
##
## REML criterion at convergence: 393.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.51696 -0.31458 -0.01041 0.24104 2.61582
##
## Random effects:
## Groups Name Variance Std.Dev.
## UID (Intercept) 22528 150.09
## Residual 8580 92.63
## Number of obs: 48, groups: UID, 12
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1205.58 90.69 8.00 13.294
## poly(Olaparib, 3)1 -978.05 185.26 24.00 -5.279
## poly(Olaparib, 3)2 21.62 185.26 24.00 0.117
## poly(Olaparib, 3)3 159.70 185.26 24.00 0.862
## BRCAd11/d11 -738.75 128.25 8.00 -5.760
## siRNAsiALC1 -296.50 128.25 8.00 -2.312
## poly(Olaparib, 3)1:BRCAd11/d11 -1457.05 262.00 24.00 -5.561
## poly(Olaparib, 3)2:BRCAd11/d11 58.32 262.00 24.00 0.223
## poly(Olaparib, 3)3:BRCAd11/d11 -347.75 262.00 24.00 -1.327
## poly(Olaparib, 3)1:siRNAsiALC1 -1413.25 262.00 24.00 -5.394
## poly(Olaparib, 3)2:siRNAsiALC1 407.20 262.00 24.00 1.554
## poly(Olaparib, 3)3:siRNAsiALC1 -374.03 262.00 24.00 -1.428
## BRCAd11/d11:siRNAsiALC1 128.42 181.38 8.00 0.708
## poly(Olaparib, 3)1:BRCAd11/d11:siRNAsiALC1 2030.29 370.52 24.00 5.480
## poly(Olaparib, 3)2:BRCAd11/d11:siRNAsiALC1 -195.54 370.52 24.00 -0.528
## poly(Olaparib, 3)3:BRCAd11/d11:siRNAsiALC1 509.78 370.52 24.00 1.376
## Pr(>|t|)
## (Intercept) 9.79e-07 ***
## poly(Olaparib, 3)1 2.05e-05 ***
## poly(Olaparib, 3)2 0.908046
## poly(Olaparib, 3)3 0.397206
## BRCAd11/d11 0.000424 ***
## siRNAsiALC1 0.049547 *
## poly(Olaparib, 3)1:BRCAd11/d11 1.01e-05 ***
## poly(Olaparib, 3)2:BRCAd11/d11 0.825733
## poly(Olaparib, 3)3:BRCAd11/d11 0.196898
## poly(Olaparib, 3)1:siRNAsiALC1 1.54e-05 ***
## poly(Olaparib, 3)2:siRNAsiALC1 0.133221
## poly(Olaparib, 3)3:siRNAsiALC1 0.166291
## BRCAd11/d11:siRNAsiALC1 0.499046
## poly(Olaparib, 3)1:BRCAd11/d11:siRNAsiALC1 1.24e-05 ***
## poly(Olaparib, 3)2:BRCAd11/d11:siRNAsiALC1 0.602518
## poly(Olaparib, 3)3:BRCAd11/d11:siRNAsiALC1 0.181577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("AIC: ", AIC(fit12))
## AIC: 429.4881
simres <- simulateResiduals(fittedModel = fit12)
plot(simres)

Compare Results
ICtab(fit1,fit2,fit3,fit4,
fit5,fit6,fit7,fit8,
fit9,fit10,fit11,fit12,
base=T)
## AIC dAIC df
## fit11 -123.5 0.0 17
## fit7 -122.7 0.8 13
## fit3 -112.3 11.1 9
## fit6 -53.1 70.4 13
## fit10 -52.6 70.9 17
## fit2 -40.9 82.5 9
## fit12 429.5 553.0 18
## fit8 473.8 597.3 14
## fit4 540.3 663.8 10
## fit1 630.5 754.0 11
## fit5 634.7 758.2 15
## fit9 641.1 764.6 19
Final Result
fit <- fit11
output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("siRNA", paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1], sep = " in " )
rownames(output) <- gsub("BRCA", paste0(" ",levels(dataset$BRCA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs|in", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs|in", rownames(output)))], levels(dataset$BRCA)[1], sep = " in " )
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$BRCA)[1], sep = " " )
# suggested result table
kable(output, row.names = T)
| Olaparib1 in siCtrl d11/heterozyg |
-0.6824454 |
0.1149183 |
-5.9385266 |
0.0000013 |
| Olaparib2 in siCtrl d11/heterozyg |
-0.0010369 |
0.1149183 |
-0.0090225 |
0.9928572 |
| Olaparib3 in siCtrl d11/heterozyg |
0.1181898 |
0.1149183 |
1.0284684 |
0.3114411 |
| Olaparib1: d11/heterozyg vs. d11/d11 in siCtrl |
-1.7682189 |
0.1625190 |
-10.8800730 |
0.0000000 |
| Olaparib2: d11/heterozyg vs. d11/d11 in siCtrl |
0.1009862 |
0.1625190 |
0.6213805 |
0.5387510 |
| Olaparib3: d11/heterozyg vs. d11/d11 in siCtrl |
-0.3284400 |
0.1625190 |
-2.0209328 |
0.0517199 |
| Olaparib1: siCtrl vs. siALC1 in d11/heterozyg |
-0.9438987 |
0.1625190 |
-5.8079270 |
0.0000019 |
| Olaparib2: siCtrl vs. siALC1 in d11/heterozyg |
0.2632965 |
0.1625190 |
1.6200963 |
0.1150261 |
| Olaparib3: siCtrl vs. siALC1 in d11/heterozyg |
-0.2610471 |
0.1625190 |
-1.6062556 |
0.1180422 |
| Olaparib1: d11/heterozyg vs. d11/d11: siCtrl vs. siALC1 |
0.8683423 |
0.2298366 |
3.7780849 |
0.0006505 |
| Olaparib2: d11/heterozyg vs. d11/d11: siCtrl vs. siALC1 |
0.0338888 |
0.2298366 |
0.1474471 |
0.8837042 |
| Olaparib3: d11/heterozyg vs. d11/d11: siCtrl vs. siALC1 |
0.3918611 |
0.2298366 |
1.7049550 |
0.0978945 |
write.table(output, file = "Figure5A_Stats_Ref_d11_heterozyg_siCtrl.txt", quote = F, sep = "\t", row.names = T, col.names = NA)
dataset$BRCA <- relevel(dataset$BRCA, ref = "d11/d11")
fit <- lm(NormCounts2 ~ poly(Olaparib,3)*BRCA*siRNA, data = dataset)
output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("siRNA", paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1], sep = " in " )
rownames(output) <- gsub("BRCA", paste0(" ",levels(dataset$BRCA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs|in", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs|in", rownames(output)))], levels(dataset$BRCA)[1], sep = " in " )
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$BRCA)[1], sep = " " )
# suggested result table
kable(output, row.names = T)
| Olaparib1 in siCtrl d11/d11 |
-2.4506644 |
0.1149183 |
-21.3252734 |
0.0000000 |
| Olaparib2 in siCtrl d11/d11 |
0.0999493 |
0.1149183 |
0.8697422 |
0.3909187 |
| Olaparib3 in siCtrl d11/d11 |
-0.2102502 |
0.1149183 |
-1.8295622 |
0.0766464 |
| Olaparib1: d11/d11 vs. d11/heterozyg in siCtrl |
1.7682189 |
0.1625190 |
10.8800730 |
0.0000000 |
| Olaparib2: d11/d11 vs. d11/heterozyg in siCtrl |
-0.1009862 |
0.1625190 |
-0.6213805 |
0.5387510 |
| Olaparib3: d11/d11 vs. d11/heterozyg in siCtrl |
0.3284400 |
0.1625190 |
2.0209328 |
0.0517199 |
| Olaparib1: siCtrl vs. siALC1 in d11/d11 |
-0.0755564 |
0.1625190 |
-0.4649081 |
0.6451454 |
| Olaparib2: siCtrl vs. siALC1 in d11/d11 |
0.2971852 |
0.1625190 |
1.8286180 |
0.0767913 |
| Olaparib3: siCtrl vs. siALC1 in d11/d11 |
0.1308140 |
0.1625190 |
0.8049148 |
0.4268091 |
| Olaparib1: d11/d11 vs. d11/heterozyg: siCtrl vs. siALC1 |
-0.8683423 |
0.2298366 |
-3.7780849 |
0.0006505 |
| Olaparib2: d11/d11 vs. d11/heterozyg: siCtrl vs. siALC1 |
-0.0338888 |
0.2298366 |
-0.1474471 |
0.8837042 |
| Olaparib3: d11/d11 vs. d11/heterozyg: siCtrl vs. siALC1 |
-0.3918611 |
0.2298366 |
-1.7049550 |
0.0978945 |
write.table(output, file = "Figure5A_Stats_Ref_d11_d11_siCtrl.txt", quote = F, sep = "\t", row.names = T, col.names = NA)
dataset$BRCA <- relevel(dataset$BRCA, ref = "d11/heterozyg")
dataset$siRNA <- relevel(dataset$siRNA, ref = "siALC1")
fit <- lm(NormCounts2 ~ poly(Olaparib,3)*BRCA*siRNA, data = dataset)
output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("siRNA", paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1], sep = " in " )
rownames(output) <- gsub("BRCA", paste0(" ",levels(dataset$BRCA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs|in", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs|in", rownames(output)))], levels(dataset$BRCA)[1], sep = " in " )
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$BRCA)[1], sep = " " )
# suggested result table
kable(output, row.names = T)
| Olaparib1 in siALC1 d11/heterozyg |
-1.6263441 |
0.1149183 |
-14.1521758 |
0.0000000 |
| Olaparib2 in siALC1 d11/heterozyg |
0.2622596 |
0.1149183 |
2.2821396 |
0.0292728 |
| Olaparib3 in siALC1 d11/heterozyg |
-0.1428573 |
0.1149183 |
-1.2431201 |
0.2228550 |
| Olaparib1: d11/heterozyg vs. d11/d11 in siALC1 |
-0.8998767 |
0.1625190 |
-5.5370541 |
0.0000042 |
| Olaparib2: d11/heterozyg vs. d11/d11 in siALC1 |
0.1348749 |
0.1625190 |
0.8299022 |
0.4127427 |
| Olaparib3: d11/heterozyg vs. d11/d11 in siALC1 |
0.0634210 |
0.1625190 |
0.3902376 |
0.6989465 |
| Olaparib1: siALC1 vs. siCtrl in d11/heterozyg |
0.9438987 |
0.1625190 |
5.8079270 |
0.0000019 |
| Olaparib2: siALC1 vs. siCtrl in d11/heterozyg |
-0.2632965 |
0.1625190 |
-1.6200963 |
0.1150261 |
| Olaparib3: siALC1 vs. siCtrl in d11/heterozyg |
0.2610471 |
0.1625190 |
1.6062556 |
0.1180422 |
| Olaparib1: d11/heterozyg vs. d11/d11: siALC1 vs. siCtrl |
-0.8683423 |
0.2298366 |
-3.7780849 |
0.0006505 |
| Olaparib2: d11/heterozyg vs. d11/d11: siALC1 vs. siCtrl |
-0.0338888 |
0.2298366 |
-0.1474471 |
0.8837042 |
| Olaparib3: d11/heterozyg vs. d11/d11: siALC1 vs. siCtrl |
-0.3918611 |
0.2298366 |
-1.7049550 |
0.0978945 |
write.table(output, file = "Figure5A_Stats_Ref_d11_heterozyg_siALC1.txt", quote = F, sep = "\t", row.names = T, col.names = NA)
Anova
fit11a <- lm(NormCounts2 ~ poly(Olaparib,3)*BRCA*siRNA, data = dataset)
fit11b <- lm(NormCounts2 ~ poly(Olaparib,3)*BRCA+siRNA, data = dataset)
# anova table
anova(fit11a, fit11b)
## Analysis of Variance Table
##
## Model 1: NormCounts2 ~ poly(Olaparib, 3) * BRCA * siRNA
## Model 2: NormCounts2 ~ poly(Olaparib, 3) * BRCA + siRNA
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 32 0.10565
## 2 39 0.35974 -7 -0.25409 10.994 5.444e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit11c <- lm(NormCounts2 ~ poly(Olaparib,3)*siRNA*BRCA, data = dataset)
fit11d <- lm(NormCounts2 ~ poly(Olaparib,3)*siRNA+BRCA, data = dataset)
# anova table
anova(fit11c, fit11d)
## Analysis of Variance Table
##
## Model 1: NormCounts2 ~ poly(Olaparib, 3) * siRNA * BRCA
## Model 2: NormCounts2 ~ poly(Olaparib, 3) * siRNA + BRCA
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 32 0.10565
## 2 39 0.72688 -7 -0.62123 26.88 1.095e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1